Common practices in today's business world can include job performance measurement. Such measurements have long been a construct of critical concern for both administrative practice and theory testing, motivating the development of quantitative productivity measures. Job performance measurement, using quantifiable criteria, can serve crucial business needs. Practitioners and researchers in the field indicate that the job performance measuring process can commonly serve two basic purposes (McGregor, “An Uneasy Look at Performance Appraisal”, Harvard Business Review, 35, 89-94 (1957); Meyer, Kay and French, “Split Roles in Performance Appraisal”, Harvard Business Review, 43, 123-129 (1965); and Wexley, “Roles of Performance Appraisal in Organizations”, In Kerr S. (Ed.), Organizational Behavior, pp 241-259, Columbus, Ohio: Grid Publishing Co. (1979)).
First, performance measures can serve administrative purposes by providing a basis for determining compensation, salary increases, promotions, terminations, and many other administrative decisions. Podsakoff, Tudor and Skov, “Effects of Leader Contingent and Non-Contingent Reward and Punishment Behaviors on Subordinate Performance and Satisfaction”, Academy of Management Journal, 25, 810-821 (1982) showed that when supervisors establish reward contingencies for job performance, employees exhibit greater job satisfaction, motivation and commitment, and this is known to increase performance. Second, job performance measures can serve developmental purposes by helping to determine how and when to provide employees with specific job feedback, assistance and counseling to improve their future job performance. For both purposes, the company can benefit through increased employee productivity.
Understandably, conclusions about the determinants of job performance and about the decision quality based on performance measures can depend heavily upon the reliability of the performance measures used, which in turn can depend on the amount of error in obtaining the performance ratings. Rater bias can be one source of error variance. Other sources of error variance can include differences in the reliability of ratings between studies, differences in range restriction between studies, and sampling error, as summarized in the literature (Schmidt and Hunter, “Development of a General Solution to the Problem of Validity Generalization”, Journal of Applied Psychology, 62, 529-540 (1977)). A well-designed rating methodology can seek to minimize, or at the least, to estimate these quantities.
Once the components of performance have been determined, remaining key technical issues can include the construction of the weighting scheme to combine the performance components to obtain an overall measurement. The weighting scheme and combination of components can provide a single measurement of employee performance for the ease of comparing employees. Furthermore, an employee's rating can often culminate in a single management decision or action, e.g., re-training the employee, rewarding the employee, or re-assigning the employee. An evaluation methodology resulting in a single primary measure of performance can improve the decision making process. Milkovich and Newman (Compensation. Plano, Tex.: Business Publications (1987)), and Davis and Sauser (“Effects Of Alternative Weighting Methods in a Policy-Capturing Approach to Job Evaluation: A Review and Empirical Investigation”, Personnel Psychology, 44, 85-127 (1991)) describe basic approaches used in combining, or weighting the components of a job task to arrive at a single performance measure, including rational, equal and statistical weighting.
Rational weights can include numerical values chosen to reflect subjective judgment about how each task component should contribute to the overall evaluation. From an administrative perspective, this method can have appeal because of its flexibility. Inputs and negotiation can be obtained from different Subject Area Experts. Rational weights can also provide flexibility in selecting weights tailored to the unique jobs and technology under study. On the other hand, rational weights can be susceptible to personal biases that may invalidate the job evaluation.
One form of the rational weights approach can include assigning weights to the various components proportional to the average or ideal amount of time the employee spends on that task. It can be noted that rational weighting can fall under the heading of subjective weighting since management often presumes that the “importance” of a task component depends on the amount of time it takes to perform the task, and the task component time used in the weighting can be some idealized calculation, rather than an actual one, to guard against weighting based on inefficient or misguided employee performance. That is, it can be recognized that a lengthy task need not be necessarily important.
In an equal weighting approach, all factors can contribute to the overall evaluation with equal weight. This simple method has found application in a variety of behavioral research contexts. For example, studies in educational testing, employee selection, etc., have indicated that the equal weighting approach can sometimes exceed the predictive validity given by differential weighting models, including multiple regression. However, the use of equal weights with job evaluation factors appears to be a rare occurrence.
In a statistical weighting approach, a statistical method, e.g., regression, can be used to estimate weights from a data set. Many statistical approaches can be used in deriving the weights, with ordinary least squares (OLS) weighting being the most often used. However, control of sampling error can potentially cause problems with OLS weighting. This can be the case when only naturally occurring task measures and evaluations are used in the calculations.
An exemplary operation of a data services center can illustrate the difficulty in determining performance measures. Technicians at such a center can perform several tasks, depending on their job assignments. The job assignments can change quite frequently, as the center managers try to match workforce to the workload. As a result, over a period of time, the technicians can work on several different job assignments, performing several tasks of various levels of difficulty. A methodology of measuring the productivity of the technicians for a certain period of time can consider the mix of job assignments such that technician productivity can be consistently compared across the data services center employees. Productivity measurements can include assignment-wise productivity measures, e.g., separate measures for separate assignments. More importantly, the productivity measurements can take into account a technician's mix of job assignments over the time period being examined.
As a further example, the basic job assignments at the center can include customer queuing and ticket pooling. Customer queuing tasks can include answering incoming calls from customers and opening trouble tickets. Ticket pooling tasks can include coordinating troubleshooting and closing the ticket once trouble has been resolved. At times when assigned workforce and workload do not match, technicians from a given nominal task assignment can work on a different task assignment. For example, a technician assigned to customer queuing can work on ticket pooling during idle customer queuing periods. The flexible working assignments, as mentioned earlier, can complicate the measurement of data services center technician performance.
In determining performance measures, as illustrated by the exemplary data services center, several issues can be taken into consideration. First, the weight of each task used to calculate the assignment-wise and total productivity score can be chosen so that employees with different mixes of job assignments can be compared fairly and consistently. As exemplified above, employees having different job assignments can work on the same tasks from time to time. Since the job activity (e.g., the relative importance of each task) for each assignment can be different, the weight of a given task can be different for different job assignments. To have productivity scores comparable across tasks, job assignments can have their own model for the assignment-wise productivity score. Second, over the period of time in which a productivity score is to be generated, employees can work on several assignments. A measurement method can be capable of combining the scores of these different assignments into a single productivity score.